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First presentation – Audience, Data, Story

This week, we gave a first presentation in class about the plans for our final visualisation.

As you can see in the slides, our chosen topic for the visualisation are music charts. For now, we parsed the Shazam website to gather data. Our dataset now consists of the top 100 songs (with artist) for 51 countries and a worldwide ranking.

The main audience we are targeting are music lovers: people who just want to explore what is popular right now, and which music would be interesting to download. Another use could be for a DJ who has a different audience than he is used to (another nationality), who could then quickly explore which music would be interesting to add to his play list.
Another user group could be artists’ managers or artists themselves, who could explore popularity of their songs of competitors’. However, we don’t know how relevant this dataset would be for them. It is clear that we focus on an explorative visualisation.

One of the first visualisation ideas that came to mind is the possiblity to compare charts from different countries by selecting them on a map. Furthermore, you could also select an artist or a certain song and check its popularity worldwide, the darkest colour would then represent the country where the song/artist ranks highest.

Of course, a lot more visualisations are possible and interesting, so this is only the beginning. This is also the main point of feedback that we got from our class mates, together with the suggestion to expand our dataset. We were already looking into expanding it, for example with historical data, and will certainly continue to work on it.

A few suggestions that were made in class:

The mean ranking for a continent would be interesting

The first visualisation in the slides is actually just another way of representing what is on the Shazam website

It would be interesting to show a trend for the top 100, are there any constants, what is the progress over time,…

Visualise which genres are popular in which countries

Compare with data from LastFM, Spotify, iTunes,…

These were all very useful suggestions and we are certainly taking them into consideration to improve our work.